Overview

Dataset statistics

Number of variables87
Number of observations176
Missing cells38
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory138.8 KiB
Average record size in memory807.7 B

Variable types

Numeric6
Categorical81

Alerts

EstateType has constant value ""Constant
DistributionType has constant value ""Constant
Object_price is highly overall correlated with LivingSpace and 12 other fieldsHigh correlation
LivingSpace is highly overall correlated with Object_price and 6 other fieldsHigh correlation
Rooms is highly overall correlated with Object_price and 2 other fieldsHigh correlation
ConstructionYear is highly overall correlated with bidet and 5 other fieldsHigh correlation
abstellraum is highly overall correlated with kable_sat_tv and 1 other fieldsHigh correlation
bidet is highly overall correlated with Object_price and 2 other fieldsHigh correlation
carport is highly overall correlated with Object_priceHigh correlation
dusche is highly overall correlated with fliesenHigh correlation
elektro is highly overall correlated with Object_price and 4 other fieldsHigh correlation
erdwaerme is highly overall correlated with Object_price and 6 other fieldsHigh correlation
fliesen is highly overall correlated with dusche and 1 other fieldsHigh correlation
frei is highly overall correlated with kunststofffensterHigh correlation
fu\u00dfbodenheizung is highly overall correlated with kontrollierte_be-_und_entl\u00fcftungsanlageHigh correlation
gaestewc is highly overall correlated with LivingSpaceHigh correlation
garage is highly overall correlated with Object_price and 3 other fieldsHigh correlation
kable_sat_tv is highly overall correlated with abstellraum and 1 other fieldsHigh correlation
kamera is highly overall correlated with ConstructionYearHigh correlation
kfw40 is highly overall correlated with ConstructionYearHigh correlation
kontrollierte_be-_und_entl\u00fcftungsanlage is highly overall correlated with Object_price and 4 other fieldsHigh correlation
kunststofffenster is highly overall correlated with fliesen and 1 other fieldsHigh correlation
luftwp is highly overall correlated with Object_price and 2 other fieldsHigh correlation
ofenheizung is highly overall correlated with ConstructionYearHigh correlation
pantry is highly overall correlated with ConstructionYearHigh correlation
parkett is highly overall correlated with Object_price and 1 other fieldsHigh correlation
reinigung is highly overall correlated with Object_price and 5 other fieldsHigh correlation
rollstuhlgerecht is highly overall correlated with Object_price and 1 other fieldsHigh correlation
speisekammer is highly overall correlated with Object_price and 1 other fieldsHigh correlation
stein is highly overall correlated with ConstructionYearHigh correlation
als_ferienimmobilie_geeignet is highly imbalanced (94.9%)Imbalance
altbau_(bis_1945) is highly imbalanced (68.5%)Imbalance
bad/wc_getrennt is highly imbalanced (78.5%)Imbalance
barriefrei is highly imbalanced (64.1%)Imbalance
bidet is highly imbalanced (94.9%)Imbalance
carport is highly imbalanced (94.9%)Imbalance
dachgeschoss is highly imbalanced (54.2%)Imbalance
dielen is highly imbalanced (91.0%)Imbalance
dsl is highly imbalanced (64.1%)Imbalance
duplex is highly imbalanced (91.0%)Imbalance
elektro is highly imbalanced (64.1%)Imbalance
erdgeschoss is highly imbalanced (54.2%)Imbalance
erdwaerme is highly imbalanced (75.9%)Imbalance
erstbezug is highly imbalanced (68.5%)Imbalance
estrich is highly imbalanced (94.9%)Imbalance
etagenheizung is highly imbalanced (50.6%)Imbalance
fern is highly imbalanced (59.9%)Imbalance
ferne is highly imbalanced (52.4%)Imbalance
garage is highly imbalanced (58.0%)Imbalance
garten is highly imbalanced (56.1%)Imbalance
gartennutzung is highly imbalanced (64.1%)Imbalance
haustiere_erlaubt is highly imbalanced (81.4%)Imbalance
holzfenster is highly imbalanced (64.1%)Imbalance
kamera is highly imbalanced (91.0%)Imbalance
kamin is highly imbalanced (87.5%)Imbalance
kfw40 is highly imbalanced (91.0%)Imbalance
kfw55 is highly imbalanced (94.9%)Imbalance
kfw70 is highly imbalanced (94.9%)Imbalance
klimatisiert is highly imbalanced (91.0%)Imbalance
kontrollierte_be-_und_entl\u00fcftungsanlage is highly imbalanced (59.9%)Imbalance
kunststoff is highly imbalanced (78.5%)Imbalance
linoleum is highly imbalanced (84.4%)Imbalance
loggia is highly imbalanced (84.4%)Imbalance
luftwp is highly imbalanced (50.6%)Imbalance
marmor is highly imbalanced (94.9%)Imbalance
massivhaus is highly imbalanced (81.4%)Imbalance
neuwertig is highly imbalanced (81.4%)Imbalance
oel is highly imbalanced (58.0%)Imbalance
ofenheizung is highly imbalanced (94.9%)Imbalance
pantry is highly imbalanced (94.9%)Imbalance
pellet is highly imbalanced (91.0%)Imbalance
reinigung is highly imbalanced (56.1%)Imbalance
rollstuhlgerecht is highly imbalanced (91.0%)Imbalance
sat is highly imbalanced (75.9%)Imbalance
speisekammer is highly imbalanced (87.5%)Imbalance
stein is highly imbalanced (91.0%)Imbalance
teilweise_m\u00f6bliert is highly imbalanced (81.4%)Imbalance
teppich is highly imbalanced (87.5%)Imbalance
tiefgarage is highly imbalanced (66.3%)Imbalance
vermietet is highly imbalanced (87.5%)Imbalance
wasch_trockenraum is highly imbalanced (64.1%)Imbalance
wg_geeignet is highly imbalanced (68.5%)Imbalance
wintergarten is highly imbalanced (84.4%)Imbalance
wohnberechtigungsschein is highly imbalanced (87.5%)Imbalance
ConstructionYear has 38 (21.6%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2023-07-11 15:01:36.655179
Analysis finished2023-07-11 15:02:09.672098
Duration33.02 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct176
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.5
Minimum0
Maximum175
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T17:02:09.820137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.75
Q143.75
median87.5
Q3131.25
95-th percentile166.25
Maximum175
Range175
Interquartile range (IQR)87.5

Descriptive statistics

Standard deviation50.950957
Coefficient of variation (CV)0.58229665
Kurtosis-1.2
Mean87.5
Median Absolute Deviation (MAD)44
Skewness0
Sum15400
Variance2596
MonotonicityStrictly increasing
2023-07-11T17:02:10.002218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.6%
1 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
Other values (166) 166
94.3%
ValueCountFrequency (%)
0 1
0.6%
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
ValueCountFrequency (%)
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%

Object_price
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean926.10239
Minimum220
Maximum3601.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T17:02:10.185046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile328.75
Q1577.5
median800
Q31096.25
95-th percentile1961.25
Maximum3601.44
Range3381.44
Interquartile range (IQR)518.75

Descriptive statistics

Standard deviation548.58475
Coefficient of variation (CV)0.59235864
Kurtosis5.9063023
Mean926.10239
Median Absolute Deviation (MAD)245.005
Skewness2.0805872
Sum162994.02
Variance300945.23
MonotonicityNot monotonic
2023-07-11T17:02:10.356279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
750 5
 
2.8%
1500 4
 
2.3%
900 4
 
2.3%
1100 4
 
2.3%
560 3
 
1.7%
450 3
 
1.7%
800 3
 
1.7%
990 3
 
1.7%
680 3
 
1.7%
340 3
 
1.7%
Other values (117) 141
80.1%
ValueCountFrequency (%)
220 2
1.1%
265 1
 
0.6%
275 1
 
0.6%
285 1
 
0.6%
300 1
 
0.6%
310 1
 
0.6%
320 1
 
0.6%
325 1
 
0.6%
330 2
1.1%
340 3
1.7%
ValueCountFrequency (%)
3601.44 1
0.6%
3163.32 1
0.6%
3030.93 1
0.6%
2880 1
0.6%
2545.6 1
0.6%
2355 1
0.6%
2220 2
1.1%
2175 1
0.6%
1890 1
0.6%
1797.71 1
0.6%

LivingSpace
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.585682
Minimum9.6
Maximum225.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T17:02:10.552174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.6
5-th percentile15.2275
Q148
median68
Q388.25
95-th percentile150.825
Maximum225.09
Range215.49
Interquartile range (IQR)40.25

Descriptive statistics

Standard deviation40.462779
Coefficient of variation (CV)0.56523565
Kurtosis3.0240573
Mean71.585682
Median Absolute Deviation (MAD)20
Skewness1.3632288
Sum12599.08
Variance1637.2365
MonotonicityNot monotonic
2023-07-11T17:02:10.902927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 5
 
2.8%
55 5
 
2.8%
90 4
 
2.3%
70 4
 
2.3%
75 4
 
2.3%
65 4
 
2.3%
50 4
 
2.3%
79 3
 
1.7%
85 3
 
1.7%
28 3
 
1.7%
Other values (106) 137
77.8%
ValueCountFrequency (%)
9.6 1
0.6%
9.8 1
0.6%
10 1
0.6%
11.7 1
0.6%
14.16 2
1.1%
14.38 1
0.6%
15.1 1
0.6%
15.13 1
0.6%
15.26 2
1.1%
18 1
0.6%
ValueCountFrequency (%)
225.09 1
0.6%
218.16 1
0.6%
213 2
1.1%
195 1
0.6%
178.29 1
0.6%
170 1
0.6%
159.1 1
0.6%
153.3 1
0.6%
150 2
1.1%
145 1
0.6%

Rooms
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4289773
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T17:02:11.046179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1272272
Coefficient of variation (CV)0.46407484
Kurtosis0.86439333
Mean2.4289773
Median Absolute Deviation (MAD)1
Skewness0.86586981
Sum427.5
Variance1.2706412
MonotonicityNot monotonic
2023-07-11T17:02:11.186699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 58
33.0%
3 48
27.3%
1 35
19.9%
4 11
 
6.2%
5 6
 
3.4%
1.5 5
 
2.8%
3.5 5
 
2.8%
2.5 4
 
2.3%
6 3
 
1.7%
5.5 1
 
0.6%
ValueCountFrequency (%)
1 35
19.9%
1.5 5
 
2.8%
2 58
33.0%
2.5 4
 
2.3%
3 48
27.3%
3.5 5
 
2.8%
4 11
 
6.2%
5 6
 
3.4%
5.5 1
 
0.6%
6 3
 
1.7%
ValueCountFrequency (%)
6 3
 
1.7%
5.5 1
 
0.6%
5 6
 
3.4%
4 11
 
6.2%
3.5 5
 
2.8%
3 48
27.3%
2.5 4
 
2.3%
2 58
33.0%
1.5 5
 
2.8%
1 35
19.9%

ConstructionYear
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)41.3%
Missing38
Missing (%)21.6%
Infinite0
Infinite (%)0.0%
Mean1986
Minimum1708
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T17:02:11.356706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1708
5-th percentile1920
Q11970
median1990
Q32019
95-th percentile2023
Maximum2023
Range315
Interquartile range (IQR)49

Descriptive statistics

Standard deviation39.37523
Coefficient of variation (CV)0.0198264
Kurtosis17.149836
Mean1986
Median Absolute Deviation (MAD)26
Skewness-2.9278148
Sum274068
Variance1550.4088
MonotonicityNot monotonic
2023-07-11T17:02:11.537471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 12
 
6.8%
2019 11
 
6.2%
2023 8
 
4.5%
1970 7
 
4.0%
1980 5
 
2.8%
2015 5
 
2.8%
1974 4
 
2.3%
2020 4
 
2.3%
1985 4
 
2.3%
1950 4
 
2.3%
Other values (47) 74
42.0%
(Missing) 38
21.6%
ValueCountFrequency (%)
1708 1
0.6%
1895 1
0.6%
1900 2
1.1%
1908 1
0.6%
1915 1
0.6%
1920 2
1.1%
1929 1
0.6%
1930 1
0.6%
1934 1
0.6%
1935 1
0.6%
ValueCountFrequency (%)
2023 8
4.5%
2022 12
6.8%
2021 3
 
1.7%
2020 4
 
2.3%
2019 11
6.2%
2018 2
 
1.1%
2017 1
 
0.6%
2016 2
 
1.1%
2015 5
2.8%
2013 1
 
0.6%

ZipCode
Real number (ℝ)

Distinct25
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97129.324
Minimum97070
Maximum97299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-07-11T17:02:11.702643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum97070
5-th percentile97070
Q197074
median97080
Q397218
95-th percentile97276.75
Maximum97299
Range229
Interquartile range (IQR)144

Descriptive statistics

Standard deviation79.244668
Coefficient of variation (CV)0.00081586759
Kurtosis-0.89712807
Mean97129.324
Median Absolute Deviation (MAD)8
Skewness0.92073731
Sum17094761
Variance6279.7174
MonotonicityNot monotonic
2023-07-11T17:02:11.853346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
97074 26
14.8%
97070 25
14.2%
97078 15
8.5%
97082 15
8.5%
97084 11
 
6.2%
97218 11
 
6.2%
97080 11
 
6.2%
97072 10
 
5.7%
97204 8
 
4.5%
97249 7
 
4.0%
Other values (15) 37
21.0%
ValueCountFrequency (%)
97070 25
14.2%
97072 10
 
5.7%
97074 26
14.8%
97076 6
 
3.4%
97078 15
8.5%
97080 11
6.2%
97082 15
8.5%
97084 11
6.2%
97204 8
 
4.5%
97209 4
 
2.3%
ValueCountFrequency (%)
97299 4
2.3%
97297 4
2.3%
97288 1
 
0.6%
97273 1
 
0.6%
97270 2
 
1.1%
97265 1
 
0.6%
97261 2
 
1.1%
97250 1
 
0.6%
97249 7
4.0%
97246 2
 
1.1%

EstateType
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
APARTMENT
176 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1584
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAPARTMENT
2nd rowAPARTMENT
3rd rowAPARTMENT
4th rowAPARTMENT
5th rowAPARTMENT

Common Values

ValueCountFrequency (%)
APARTMENT 176
100.0%

Length

2023-07-11T17:02:12.009585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:12.172561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
apartment 176
100.0%

Most occurring characters

ValueCountFrequency (%)
A 352
22.2%
T 352
22.2%
P 176
11.1%
R 176
11.1%
M 176
11.1%
E 176
11.1%
N 176
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1584
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 352
22.2%
T 352
22.2%
P 176
11.1%
R 176
11.1%
M 176
11.1%
E 176
11.1%
N 176
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1584
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 352
22.2%
T 352
22.2%
P 176
11.1%
R 176
11.1%
M 176
11.1%
E 176
11.1%
N 176
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 352
22.2%
T 352
22.2%
P 176
11.1%
R 176
11.1%
M 176
11.1%
E 176
11.1%
N 176
11.1%

DistributionType
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
RENT
176 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters704
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT 176
100.0%

Length

2023-07-11T17:02:12.305018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:12.461499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rent 176
100.0%

Most occurring characters

ValueCountFrequency (%)
R 176
25.0%
E 176
25.0%
N 176
25.0%
T 176
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 704
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 176
25.0%
E 176
25.0%
N 176
25.0%
T 176
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 704
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 176
25.0%
E 176
25.0%
N 176
25.0%
T 176
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 176
25.0%
E 176
25.0%
N 176
25.0%
T 176
25.0%

abstellraum
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
147 
1
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Length

2023-07-11T17:02:12.629331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:12.794026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

als_ferienimmobilie_geeignet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T17:02:12.932323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:13.092079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

altbau_(bis_1945)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
166 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Length

2023-07-11T17:02:13.225197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:13.379206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

bad/wc_getrennt
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
170 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Length

2023-07-11T17:02:13.517982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:13.672618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

balkon
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
104 
1
72 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

Length

2023-07-11T17:02:13.804886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:13.959388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

Most occurring characters

ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 104
59.1%
1 72
40.9%

barriefrei
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T17:02:14.093480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:14.250055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

bidet
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T17:02:14.378683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:14.534292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

carport
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T17:02:14.661649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:14.817449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

dachgeschoss
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
159 
1
17 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Length

2023-07-11T17:02:14.946506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:15.107457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

dielen
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T17:02:15.239477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:15.399060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

dsl
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T17:02:15.525736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:15.686493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

duplex
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T17:02:15.816188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:15.973675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

dusche
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
102 
1
74 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Length

2023-07-11T17:02:16.104785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:16.261939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring characters

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

einbauk\u00fcche
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
92 
1
84 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

Length

2023-07-11T17:02:16.402994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:16.558858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

Most occurring characters

ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92
52.3%
1 84
47.7%

elektro
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T17:02:16.690305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:16.850427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

erdgeschoss
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
159 
1
17 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Length

2023-07-11T17:02:16.977360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:17.128670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 159
90.3%
1 17
 
9.7%

erdwaerme
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
169 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Length

2023-07-11T17:02:17.264251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:17.415936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

erstbezug
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
166 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Length

2023-07-11T17:02:17.542843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:17.695666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

estrich
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T17:02:17.833502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:17.985893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

etagenheizung
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
157 
1
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Length

2023-07-11T17:02:18.115590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:18.267788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

fenster
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
100 
1
76 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

Length

2023-07-11T17:02:18.403451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:18.556641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

Most occurring characters

ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 100
56.8%
1 76
43.2%

fern
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
162 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Length

2023-07-11T17:02:18.688791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:18.848240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

ferne
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
158 
1
18 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

Length

2023-07-11T17:02:18.979635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:19.136505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 158
89.8%
1 18
 
10.2%

fliesen
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
112 
1
64 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

Length

2023-07-11T17:02:19.268442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:19.430345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

Most occurring characters

ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 112
63.6%
1 64
36.4%

frei
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
146 
1
30 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

Length

2023-07-11T17:02:19.563598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:19.722702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

Most occurring characters

ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 146
83.0%
1 30
 
17.0%

fu\u00dfbodenheizung
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
143 
1
33 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

Length

2023-07-11T17:02:19.857562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:20.195970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 143
81.2%
1 33
 
18.8%

gaestewc
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
145 
1
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Length

2023-07-11T17:02:20.336740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:20.492096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring characters

ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

garage
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
161 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Length

2023-07-11T17:02:20.623432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:20.782426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

garten
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
160 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Length

2023-07-11T17:02:20.911538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:21.065112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

gartennutzung
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T17:02:21.207805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:21.394106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

gas
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
102 
1
74 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Length

2023-07-11T17:02:21.550166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:21.735008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring characters

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

gepflegt
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
150 
1
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

Length

2023-07-11T17:02:21.879903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:22.038360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 150
85.2%
1 26
 
14.8%

haustiere_erlaubt
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
171 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Length

2023-07-11T17:02:22.171018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:22.325499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

holzfenster
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T17:02:22.454938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:22.608240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

kable_sat_tv
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
145 
1
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Length

2023-07-11T17:02:22.738240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:22.893584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring characters

ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 145
82.4%
1 31
 
17.6%

kamera
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T17:02:23.031931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:23.186419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

kamin
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
173 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Length

2023-07-11T17:02:23.322129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:23.482760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

kelleranteil
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
90 
1
86 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

Length

2023-07-11T17:02:23.613545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:23.775226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

Most occurring characters

ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 90
51.1%
1 86
48.9%

kfw40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T17:02:23.916005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:24.073438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

kfw55
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T17:02:24.207963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:24.366122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

kfw70
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T17:02:24.501932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:24.657963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

klimatisiert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T17:02:24.785691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:24.939365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

kontrollierte_be-_und_entl\u00fcftungsanlage
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
162 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Length

2023-07-11T17:02:25.071269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:25.225868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 162
92.0%
1 14
 
8.0%

kunststoff
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
170 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Length

2023-07-11T17:02:25.357023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:25.513789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 170
96.6%
1 6
 
3.4%

kunststofffenster
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
137 
1
39 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

Length

2023-07-11T17:02:25.643209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:25.793972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 137
77.8%
1 39
 
22.2%

laminat
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
147 
1
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Length

2023-07-11T17:02:25.923767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:26.076102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147
83.5%
1 29
 
16.5%

linoleum
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
172 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Length

2023-07-11T17:02:26.211982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:26.366905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

loggia
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
172 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Length

2023-07-11T17:02:26.493482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:26.650617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

luftwp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
157 
1
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Length

2023-07-11T17:02:26.781115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:26.938469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157
89.2%
1 19
 
10.8%

marmor
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T17:02:27.223042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:27.664388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

massivhaus
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
171 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Length

2023-07-11T17:02:27.969697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:28.173919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

moebliert
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
151 
1
25 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Length

2023-07-11T17:02:28.327011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:28.499124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

neubau
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
151 
1
25 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Length

2023-07-11T17:02:28.653484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:28.812330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 151
85.8%
1 25
 
14.2%

neuwertig
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
171 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Length

2023-07-11T17:02:28.947237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:29.096436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

oel
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
161 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Length

2023-07-11T17:02:29.222999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:29.372197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 161
91.5%
1 15
 
8.5%

ofenheizung
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T17:02:29.501439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:29.649872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
156 
1
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

Length

2023-07-11T17:02:29.772591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:29.925172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 156
88.6%
1 20
 
11.4%

pantry
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
175 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Length

2023-07-11T17:02:30.075850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:30.233531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175
99.4%
1 1
 
0.6%

parkett
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
139 
1
37 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

Length

2023-07-11T17:02:30.360298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:30.520029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

Most occurring characters

ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 139
79.0%
1 37
 
21.0%

pellet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T17:02:30.798341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:30.949855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

personenaufzug
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
125 
1
51 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

Length

2023-07-11T17:02:31.083439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:31.231805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

Most occurring characters

ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125
71.0%
1 51
29.0%

reinigung
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
160 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Length

2023-07-11T17:02:31.360091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:31.513387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 160
90.9%
1 16
 
9.1%

renoviert
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
154 
1
22 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

Length

2023-07-11T17:02:31.637613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:31.788131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

Most occurring characters

ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 154
87.5%
1 22
 
12.5%

rollstuhlgerecht
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T17:02:31.920167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:32.070109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

sat
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
169 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Length

2023-07-11T17:02:32.219399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:32.369122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 169
96.0%
1 7
 
4.0%

speisekammer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
173 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Length

2023-07-11T17:02:32.501826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:32.675534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

stein
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
174 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Length

2023-07-11T17:02:32.819200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:32.983648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
98.9%
1 2
 
1.1%

stellplatz
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
152 
1
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

Length

2023-07-11T17:02:33.117693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:33.281979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

Most occurring characters

ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 152
86.4%
1 24
 
13.6%

teilweise_m\u00f6bliert
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
171 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Length

2023-07-11T17:02:33.430430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:33.587039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 171
97.2%
1 5
 
2.8%

teppich
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
173 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Length

2023-07-11T17:02:33.717893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:33.894089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

terrasse
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
138 
1
38 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

Length

2023-07-11T17:02:34.038141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:34.240433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

Most occurring characters

ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 138
78.4%
1 38
 
21.6%

tiefgarage
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
165 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

Length

2023-07-11T17:02:34.388863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:34.579239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 165
93.8%
1 11
 
6.2%

vermietet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
173 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Length

2023-07-11T17:02:34.731953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:34.917362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

wanne
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
102 
1
74 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Length

2023-07-11T17:02:35.084573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:35.265806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring characters

ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 102
58.0%
1 74
42.0%

wasch_trockenraum
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
164 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Length

2023-07-11T17:02:35.421506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:35.581964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164
93.2%
1 12
 
6.8%

wg_geeignet
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
166 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Length

2023-07-11T17:02:35.733525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:35.904164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 166
94.3%
1 10
 
5.7%

wintergarten
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
172 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Length

2023-07-11T17:02:36.055407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:36.251217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 172
97.7%
1 4
 
2.3%

wohnberechtigungsschein
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
173 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Length

2023-07-11T17:02:36.404883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:36.606502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173
98.3%
1 3
 
1.7%

zentralheizung
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
1
90 
0
86 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Length

2023-07-11T17:02:36.764878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T17:02:36.940376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Most occurring characters

ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 90
51.1%
0 86
48.9%

Interactions

2023-07-11T17:02:07.311467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:03.055906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:03.913503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:04.875252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:05.648606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:06.482892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:07.458337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:03.202294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:04.063408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:05.010425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:05.791193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:06.629387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:07.595760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:03.345617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:04.208474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:05.148522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:05.926513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:06.770908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:07.720781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:03.487967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:04.341279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:05.265684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:06.051301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:06.901842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:07.852632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:03.633583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:04.479740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:05.391579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:06.175148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:07.038044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:07.992205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:03.775397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:04.622029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:05.526076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:06.350027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-11T17:02:07.180267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-11T17:02:37.253890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeabstellraumals_ferienimmobilie_geeignetaltbau_(bis_1945)bad/wc_getrenntbalkonbarriefreibidetcarportdachgeschossdielendslduplexduscheeinbauk\u00fccheelektroerdgeschosserdwaermeerstbezugestrichetagenheizungfensterfernfernefliesenfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegthaustiere_erlaubtholzfensterkable_sat_tvkamerakaminkelleranteilkfw40kfw55kfw70klimatisiertkontrollierte_be-_und_entl\u00fcftungsanlagekunststoffkunststofffensterlaminatlinoleumloggialuftwpmarmormassivhausmoebliertneubauneuwertigoelofenheizungoffene_k\u00fcchepantryparkettpelletpersonenaufzugreinigungrenoviertrollstuhlgerechtsatspeisekammersteinstellplatzteilweise_m\u00f6bliertteppichterrassetiefgaragevermietetwannewasch_trockenraumwg_geeignetwintergartenwohnberechtigungsscheinzentralheizung
Unnamed: 01.0000.0400.0840.0850.034-0.0430.0540.0000.0000.0000.0000.0000.0000.0000.1710.0000.0830.0000.3840.1930.3570.1070.3870.2640.0000.1030.2770.1450.2670.3060.2980.0000.0000.2920.2310.1280.2600.0000.0000.1800.3700.0000.1630.0000.0000.0450.0450.2230.3340.0000.2980.0000.0000.0000.1900.0450.2160.0970.0000.0080.1630.0000.1150.0450.3130.0000.1980.3270.1100.0000.1270.1630.0000.1500.1580.0000.2250.2510.0000.1290.1730.0000.1180.0000.284
Object_price0.0401.0000.7970.6810.1440.0510.4700.0000.0000.3230.1930.3590.6700.5290.0870.0000.0000.0000.2060.1120.5880.0000.8090.1900.0000.1050.1670.2470.2980.2330.3780.4310.4150.5850.2600.2300.0000.0000.0000.1730.4000.4720.0000.2090.0000.0760.0000.0000.5830.0000.2980.0000.0000.0000.5030.0000.0000.0000.1540.0000.0000.0000.2290.0000.5190.2530.3810.6540.0000.6910.0000.6380.4520.0000.0800.0900.3120.0000.0000.2980.0790.0000.0000.0000.221
LivingSpace0.0840.7971.0000.8650.1080.3160.4850.0000.0000.3230.3600.4600.6700.4420.1630.0000.0780.1100.1030.1560.4870.0000.6850.0000.0000.0000.2830.1410.3070.1630.3310.4070.5300.4640.2990.2630.1880.0600.0000.2280.4170.4680.1820.3330.1100.0000.0000.0000.4450.0000.2840.0000.0470.0000.4320.0000.0000.3260.1830.0000.0000.0000.0000.0000.4940.2230.3230.6360.0000.6880.0000.3210.4510.0000.0000.1420.3120.2420.0000.3780.1580.0000.0000.0000.290
Rooms0.0850.6810.8651.0000.0800.3150.3400.0000.0000.2870.1930.1580.0000.3420.1300.2340.0000.0000.0000.1380.2900.0000.4220.0000.0000.0800.3320.2030.3270.0000.2730.2750.4500.3160.1600.0000.1770.0000.0000.0000.2350.0000.1480.3470.0000.0000.0000.0000.2670.0000.1650.0000.0000.0000.3830.0000.0000.3720.0000.0000.0000.0000.0000.0000.3350.0000.1390.3350.1400.4620.0000.5840.0000.1500.0000.0000.2190.0690.2130.4080.0000.0000.0000.0000.226
ConstructionYear0.0340.1440.1080.0801.0000.1550.1170.0000.4290.3850.1890.2800.9850.0000.0000.2490.4190.0000.0000.0350.0630.1900.1420.0000.3540.3320.2440.2720.0000.0000.0770.2830.1760.0000.0000.0520.2020.1930.1570.4520.1880.6830.0970.1971.0000.0000.0000.0000.1850.0000.0000.2230.0000.0000.3210.0000.0970.0000.3140.0580.1761.0000.1960.9850.1930.0000.3720.3410.2170.0000.2110.0000.7380.0000.4340.0000.0150.0000.0000.1610.2310.1370.0000.0000.138
ZipCode-0.0430.0510.3160.3150.1551.0000.0890.3660.0000.0820.0000.0000.0000.2790.1470.0000.0300.0000.0240.2050.0000.0000.0000.3530.0000.0000.2330.0080.0000.1070.0000.0000.0630.0490.0000.0680.0000.0000.0650.0000.1390.0000.0000.2320.0000.1470.0000.2700.0000.0000.0880.0000.1010.0690.1360.0000.0000.1110.0000.0000.2740.0000.1530.0000.0000.0000.2120.0000.0000.1410.2090.1160.0000.0000.0000.0560.1780.2100.0000.1270.0480.0000.0000.0000.117
abstellraum0.0540.4700.4850.3400.1170.0891.0000.0000.0000.2870.0850.0540.0000.0000.1180.0000.2650.0000.4070.0000.3280.0000.4130.0000.0000.0000.2030.0000.2700.3090.3410.3080.1600.3230.0000.0000.0000.1170.0000.0540.5350.1520.0000.1790.0000.0000.0000.1520.3430.1030.4020.0000.0420.1740.1490.0000.0000.1400.0000.0000.0000.0000.0750.0000.4610.1520.2280.5220.0000.0000.0000.2260.0000.1890.0000.0000.0000.0760.0920.1470.0000.0000.0000.0000.000
als_ferienimmobilie_geeignet0.0000.0000.0000.0000.0000.3660.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0170.0000.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
altbau_(bis_1945)0.0000.0000.0000.0000.4290.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0480.0250.0000.0380.0390.0000.0000.0000.0000.0000.0830.1930.2340.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.1630.0000.0000.0000.0000.1040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.3020.0000.0000.000
bad/wc_getrennt0.0000.3230.3230.2870.3850.0820.2870.0000.0001.0000.0000.1130.1790.1790.0000.0000.0000.0000.1740.0000.0000.0000.0000.0000.0000.0000.0940.0000.1800.0410.0970.0810.1870.0810.0000.0000.0000.0000.0000.2490.0920.4170.0000.0000.0000.1790.0000.0000.0000.0000.0460.0000.2770.0000.0000.0000.0000.0000.0000.0000.0000.0000.2680.0000.1550.1030.0950.2000.0000.0000.0000.3300.1030.1340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
balkon0.0000.1930.3600.1930.1890.0000.0850.0000.0000.0001.0000.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.2000.0610.0000.0000.0000.1290.0040.0000.1030.0640.0470.2570.0620.0320.0000.0640.0000.0670.0000.0000.0000.0000.2510.0000.0000.0000.0000.0910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1830.0000.0000.2770.0000.0000.0000.0850.000
barriefrei0.0000.3590.4600.1580.2800.0000.0540.0000.0000.1130.0921.0000.1050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2860.1520.0000.0440.1060.2490.0000.0000.0000.0000.0000.0000.0000.0000.2810.0000.0000.0000.1050.0000.0000.0000.0000.0000.0490.1700.0000.0440.0000.0000.0180.3010.0000.0000.0000.0000.0000.0000.0160.2390.0810.0000.2810.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2130.000
bidet0.0000.6700.6700.0000.9850.0000.0000.0000.0000.1790.0000.1051.0000.0000.0000.0000.1050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1050.0000.3410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.0000.0000.0770.0000.0000.0000.0000.3410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
carport0.0000.5290.4420.3420.0000.2790.0000.0000.0000.1790.0000.0000.0001.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0530.0000.0000.0000.0000.0770.0000.0000.0000.2730.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
dachgeschoss0.1710.0870.1630.1300.0000.1470.1180.0000.0000.0000.0000.0000.0000.0711.0000.0000.2440.0000.0520.1070.0000.0000.0000.0000.0710.0700.0970.0000.2930.0540.1100.0000.0000.0000.0000.0000.1070.2030.0000.0690.1020.0000.0000.0000.0000.0000.0000.2250.0000.0000.2060.0000.0000.0000.0000.0000.0000.0740.0000.0000.0000.0000.2030.0000.1160.0000.0000.1070.0570.0000.0290.0000.0000.2220.0910.0000.0000.0000.0000.0000.0000.0000.0000.0000.038
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laminat0.0000.0000.0000.0000.2230.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0490.1520.1110.0310.0490.0000.0000.0000.0000.0000.0000.0000.0000.3090.0720.1350.0000.0000.0000.0000.0000.2580.0000.1340.0600.0000.0920.1090.0000.0000.0000.0000.0690.0000.0121.0000.0000.0000.1060.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.1850.0000.0000.0000.0000.0000.0000.1390.0000.0000.0000.0340.0920.0000.2010.0000.1740.0000.000
linoleum0.0000.0000.0470.0000.0000.1010.0420.0000.0000.2770.0000.1700.0000.0000.0000.0000.0000.0000.1190.0000.0000.0000.0000.0000.0000.0000.0000.1490.2530.0340.1690.0000.0000.0000.0000.0000.0000.0000.0000.1700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1280.0001.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4160.081
loggia0.0000.0000.0000.0000.0000.0690.1740.0000.0000.0000.0000.0000.0000.0000.0000.0000.1700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0910.0000.0000.0000.0000.0000.0000.1280.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1360.1450.0000.1310.0000.0000.2510.0000.0000.2040.0000.0000.0000.1820.0000.0000.0000.0000.0000.0000.000
luftwp0.1900.5030.4320.3830.3210.1360.1490.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.1070.5190.0700.5340.3420.0000.0520.0000.0000.0000.0000.1400.3170.0000.3790.0000.0000.1900.0000.0000.0000.1320.0000.0000.1760.0000.0000.0000.0000.3990.0000.1240.1060.0000.0001.0000.0000.0000.0870.0000.0000.0000.0000.0590.0000.0840.0000.2300.2950.0000.0000.0000.0000.0000.0820.0000.0000.3210.1580.0000.0550.0000.0000.0000.0000.135
marmor0.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2730.0000.0000.0000.0000.0000.0000.1790.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
massivhaus0.2160.0000.0000.0000.0970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1380.0000.0610.0000.0000.0000.0000.0000.0000.0740.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2230.0000.0000.0000.0000.0000.0000.0000.0000.2400.0000.0000.0000.0000.0000.0001.0000.0000.0170.0000.0000.2010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
moebliert0.0970.0000.3260.3720.0000.1110.1400.0170.0000.0000.0000.0180.0000.0000.0740.0000.0180.0000.2180.1290.0180.0740.0000.0000.0000.0000.2970.0480.0810.2040.1450.0000.0300.0580.0660.0000.2190.0670.0000.0180.0300.0000.0000.3420.0000.0000.0000.0000.0480.0000.0920.0000.0000.0000.0870.0000.0001.0000.0000.0170.0580.0000.0000.0000.0000.0000.0890.0660.1050.0000.0000.0000.0000.1160.0000.0000.0860.0000.0000.1470.0000.0000.0000.0000.000
neubau0.0000.1540.1830.0000.3140.0000.0000.0170.0000.0000.0000.3010.0000.0170.0000.0000.0880.1710.1080.0360.0000.0000.0000.0000.0170.0000.0000.0510.0720.0870.0000.1400.0000.0580.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0170.0000.0000.1270.2220.0000.0680.0000.0000.0000.0170.0001.0000.1590.0580.0000.1140.0000.0000.1710.0290.0000.0260.1710.1000.1120.0000.0000.0170.0000.0350.0000.0000.0000.1640.0000.0000.2500.000
neuwertig0.0080.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.1240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2010.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0170.1591.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0800.0000.0000.0000.0000.1380.0000.0000.0000.000
oel0.1630.0000.0000.0000.1760.2740.0000.0000.0000.0000.1640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.2280.1070.0000.0000.0000.0000.0000.1050.0000.0000.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0580.0580.0001.0000.0000.0000.0000.0330.0000.1030.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0500.0930.0000.1390.0000.137
ofenheizung0.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2010.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
offene_k\u00fcche0.1150.2290.0000.0000.1960.1530.0750.0530.0000.2680.0000.0000.0530.0530.2030.0000.1320.0000.3220.0000.0000.0000.0000.0000.0530.0590.1590.0000.2530.2590.1800.3010.0540.0000.0000.0000.1690.0190.0000.0000.0000.2020.0000.0000.0000.0530.0530.2020.2480.0000.3400.0000.0000.0000.0590.0000.0000.0000.1140.0660.0000.0001.0000.0000.1240.0000.0000.0730.0780.0000.2370.1420.2020.3460.0660.0000.0000.0530.0000.0000.1320.0000.0000.0000.000
pantry0.0450.0000.0000.0000.9850.0000.0000.0000.1240.0000.0000.0000.0000.0000.0000.0000.1050.0000.0000.0000.0000.0710.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.2010.0000.0000.0000.0000.0000.1050.0000.0000.0000.000
parkett0.3130.5190.4940.3350.1930.0000.4610.0000.0000.1550.0000.0000.0000.0000.1160.0000.2070.0000.3010.0000.2660.1160.3520.0000.0000.0000.1360.0270.3010.2520.2570.2230.2060.2570.0000.0000.0000.1400.0000.2070.3590.1210.0000.0980.0000.0000.0000.0000.3300.0000.2690.0000.0000.1360.0840.0000.0000.0000.0000.0000.0330.0000.1240.0001.0000.1210.1950.5360.0480.0000.1240.1870.0000.0000.0960.0000.0000.1010.0000.2120.0000.0000.0000.0000.098
pellet0.0000.2530.2230.0000.0000.0000.1520.0000.0000.1030.0000.0160.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.1430.0000.0000.0000.0000.0000.0000.0000.0000.2300.0000.0000.0000.3410.0000.0000.0000.0000.0000.0000.0000.1450.0000.0000.0000.0000.1710.0000.0000.0000.0000.0000.1211.0000.0780.2350.0000.0000.0870.1780.0000.0000.0000.0000.0000.2960.0000.0000.0000.0000.0000.0000.000
personenaufzug0.1980.3810.3230.1390.3720.2120.2280.0000.0000.0950.0000.2390.0000.0000.0000.0000.0000.0000.0000.0600.1300.0000.2770.0000.0000.0290.0860.1410.1130.0000.1020.1400.1650.1200.0000.0630.2150.0000.0240.0000.0880.0780.0000.0480.0000.0000.0000.0000.1410.0400.0000.1850.0840.0000.2300.0000.0000.0890.0290.0000.1030.0000.0000.0000.1950.0781.0000.3350.0000.0000.0000.1390.0000.0000.0000.0000.0000.2110.0000.0000.0630.0000.0000.0000.000
reinigung0.3270.6540.6360.3350.3410.0000.5220.0000.0000.2000.0000.0810.0770.0770.1070.0000.2570.0000.2610.1460.4190.1070.5900.0000.0000.0000.0710.0000.2410.3070.4030.3210.3390.3570.0000.0000.0000.0000.0000.0810.5510.2350.0000.2540.0000.0770.0000.0000.4500.0000.3720.0000.0000.1310.2950.0000.0000.0660.0000.0000.0000.0000.0730.0770.5360.2350.3351.0000.0000.0000.0000.1720.0000.1100.0990.0000.0630.2760.0000.1760.0000.0000.0000.0000.075
renoviert0.1100.0000.0000.1400.2170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.1070.1550.0000.0000.0000.0000.0000.0000.1170.0000.0000.0480.0000.0000.0000.0000.0000.0000.2050.1100.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0410.0000.1050.0260.0000.0000.0000.0780.0000.0480.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2300.0000.0000.000
rollstuhlgerecht0.0000.6910.6880.4620.0000.1410.0000.0000.0000.0000.0000.2810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1430.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
sat0.1270.0000.0000.0000.2110.2090.0000.0000.0000.0000.0000.0000.0000.0000.0290.0870.2210.0000.0000.0000.0000.0000.0000.0000.1620.0000.0430.0000.0000.0000.0000.0460.0000.0000.0000.0000.1310.0000.0000.0000.0000.0870.0000.0000.0000.1620.1620.0000.1950.0000.1140.0000.0000.2510.0000.0000.0000.0000.1000.0000.0000.0000.2370.0000.1240.0870.0000.0000.0000.0001.0000.0400.0870.2030.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.000
speisekammer0.1630.6380.3210.5840.0000.1160.2260.0000.0000.3300.0000.0000.0000.2730.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0000.0760.0000.3090.1040.0000.2050.0830.0000.0000.0000.0000.0000.0000.0000.0000.1780.0000.0000.0000.2730.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.1120.0000.0000.0000.1420.0000.1870.1780.1390.1720.0000.0000.0401.0000.0000.1180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
stein0.0000.4520.4510.0000.7380.0000.0000.0000.0000.1030.0000.0160.3410.0000.0000.2300.2810.0000.0000.0000.0000.0000.0000.0000.3410.0000.0000.0000.0000.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.2300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2020.0000.0000.0000.0000.0000.0000.0000.0870.0001.0000.0000.1220.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
stellplatz0.1500.0000.0000.1500.0000.0000.1890.0000.0000.1340.0000.0000.0000.0270.2220.0000.0970.0000.0860.1500.0000.0000.0000.0000.0000.0000.0000.0420.0830.1840.0000.0390.0000.0520.0000.0000.0290.0510.0000.0970.0000.0000.1180.0000.0000.0270.0270.1770.0000.0000.1490.1390.0000.2040.0820.0270.0000.1160.0000.0000.0420.0000.3460.0000.0000.0000.0000.1100.0000.0000.2030.1180.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.2040.0000.000
teilweise_m\u00f6bliert0.1580.0800.0000.0000.4340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0910.1220.2840.0000.0000.0000.0000.0910.0000.0000.2010.2030.1430.0000.0820.0000.0000.0000.0000.0000.0000.0000.1480.1520.0000.0000.1250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0660.2010.0960.0000.0000.0990.0000.0000.0000.0000.1220.0001.0000.0800.0000.0000.0000.0000.0000.0000.0000.0000.000
teppich0.0000.0900.1420.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0801.0000.0000.0000.0000.0000.0000.0000.0000.0000.039
terrasse0.2250.3120.3120.2190.0150.1780.0000.0000.0640.0000.1830.0000.0000.0000.0000.0000.0000.0000.0000.0000.1410.2130.0000.1850.0000.0000.1200.1050.1970.0000.0000.1350.0000.1430.3800.2590.0000.0000.0000.0000.0000.0000.0000.2060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3210.0000.0000.0860.0350.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0001.0000.2230.0000.1350.0000.0640.1320.0000.000
tiefgarage0.2510.0000.2420.0690.0000.2100.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.1450.0000.0000.0000.0000.0860.0000.2820.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0340.0000.1790.0000.1140.0000.0000.0000.0000.0890.0340.0000.1820.1580.0000.0000.0000.0000.0000.0000.0000.0530.0000.1010.2960.2110.2760.0000.0000.0000.0000.0000.0000.0000.0000.2231.0000.0000.0670.0000.0000.0000.0000.045
vermietet0.0000.0000.0000.2130.0000.0000.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1560.0000.0000.0000.0000.0000.0000.0000.0000.1070.0000.2130.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.1030.0000.039
wanne0.1290.2980.3780.4080.1610.1270.1470.0000.0000.0000.2770.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0520.1960.1120.0000.0000.3540.0000.1290.0800.1290.2120.3390.2010.0820.0830.1810.0350.0000.0000.1470.0000.0000.3220.0000.0000.0000.0000.1820.0000.0410.0000.0000.0000.0550.0000.0000.1470.0000.0000.0500.0000.0000.0000.2120.0000.0000.1760.0000.0000.0520.0000.0000.0000.0000.0000.1350.0670.0001.0000.0000.1460.1190.0000.000
wasch_trockenraum0.1730.0790.1580.0000.2310.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2810.1890.0000.0000.1620.0000.0250.0000.0440.0000.0000.0000.1260.0440.0000.0000.0000.0000.0000.0000.1560.0000.2280.1860.0000.0000.1460.0000.0000.0000.0000.0000.2490.1950.2010.0000.0000.0000.0000.0000.0000.1640.1380.0930.0000.1320.1050.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
wg_geeignet0.0000.0000.0000.0000.1370.0000.0000.0000.3020.0000.0000.0000.0000.0000.0000.0000.0000.0000.1680.1110.0000.0000.0000.0000.0000.0000.0780.0000.0000.0000.0000.0420.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.1040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2300.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.1460.0001.0000.0000.0000.000
wintergarten0.1180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1230.0000.0000.0000.0000.0000.0000.1150.1440.0000.0000.0000.0000.2740.0000.0000.0620.0000.0000.0000.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.1740.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2040.0000.0000.1320.0000.1030.1190.0000.0001.0000.0000.000
wohnberechtigungsschein0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0850.2130.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.1910.1560.0000.0870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0000.4160.0000.0000.0000.0000.0000.2500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
zentralheizung0.2840.2210.2900.2260.1380.1170.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.2430.0000.1630.0000.0000.3300.0000.1190.0000.0100.0000.2330.0000.1970.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2700.0000.0000.0000.0810.0000.1350.0000.0000.0000.0000.0000.1370.0000.0000.0000.0980.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0390.0000.0450.0390.0000.0000.0000.0000.0001.000

Missing values

2023-07-11T17:02:08.449933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-11T17:02:09.133790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeEstateTypeDistributionTypeabstellraumals_ferienimmobilie_geeignetaltbau_(bis_1945)bad/wc_getrenntbalkonbarriefreibidetcarportdachgeschossdielendslduplexduscheeinbauk\u00fccheelektroerdgeschosserdwaermeerstbezugestrichetagenheizungfensterfernfernefliesenfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegthaustiere_erlaubtholzfensterkable_sat_tvkamerakaminkelleranteilkfw40kfw55kfw70klimatisiertkontrollierte_be-_und_entl\u00fcftungsanlagekunststoffkunststofffensterlaminatlinoleumloggialuftwpmarmormassivhausmoebliertneubauneuwertigoelofenheizungoffene_k\u00fcchepantryparkettpelletpersonenaufzugreinigungrenoviertrollstuhlgerechtsatspeisekammersteinstellplatzteilweise_m\u00f6bliertteppichterrassetiefgaragevermietetwannewasch_trockenraumwg_geeignetwintergartenwohnberechtigungsscheinzentralheizung
00995.050.01.52012.097084APARTMENTRENT0000100000001100000000010100000000000000000000000001000000000000000000000000000
11475.050.02.0NaN97209APARTMENTRENT0000000000000101000100000000000100000100000001000000000000000000000000000110000
22750.070.02.01956.097297APARTMENTRENT0000000000000100000010000000001000000000000000001000000000000010000000000101001
33580.074.03.01940.097249APARTMENTRENT0010000000000110000010000000100000000100000000000000000000000010000000000001000
44780.086.03.01995.097297APARTMENTRENT0000100000001100000010000010000000000100000000000000011000000000000001000100001
55820.094.03.0NaN97297APARTMENTRENT0000000000000000000000000010111000000100000000000000000000000000000000000000000
661250.0115.03.52019.097246APARTMENTRENT0000110000000000000001000110000000000100000000000000100000001001000000000000000
77853.067.02.02021.097209APARTMENTRENT1000000010101000000000010100001000100000010010000000000010000000000100000000001
88836.079.02.02021.097209APARTMENTRENT1000000010101100000000010100001000100000010010000000000010000000000100000000001
991040.072.02.02019.097209APARTMENTRENT1000100000001100010010010000001000000000000000000000000000100000000100000100000
Unnamed: 0Object_priceLivingSpaceRoomsConstructionYearZipCodeEstateTypeDistributionTypeabstellraumals_ferienimmobilie_geeignetaltbau_(bis_1945)bad/wc_getrenntbalkonbarriefreibidetcarportdachgeschossdielendslduplexduscheeinbauk\u00fccheelektroerdgeschosserdwaermeerstbezugestrichetagenheizungfensterfernfernefliesenfreifu\u00dfbodenheizunggaestewcgaragegartengartennutzunggasgepflegthaustiere_erlaubtholzfensterkable_sat_tvkamerakaminkelleranteilkfw40kfw55kfw70klimatisiertkontrollierte_be-_und_entl\u00fcftungsanlagekunststoffkunststofffensterlaminatlinoleumloggialuftwpmarmormassivhausmoebliertneubauneuwertigoelofenheizungoffene_k\u00fcchepantryparkettpelletpersonenaufzugreinigungrenoviertrollstuhlgerechtsatspeisekammersteinstellplatzteilweise_m\u00f6bliertteppichterrassetiefgaragevermietetwannewasch_trockenraumwg_geeignetwintergartenwohnberechtigungsscheinzentralheizung
166166450.065.03.01964.097204APARTMENTRENT0000000010100100000110100000001101000000000000000000000000100000100000000100000
1671672220.0213.05.02020.097299APARTMENTRENT1001000110001100000010110110001000100100000010000000100010101100010100000100000
1681682880.0195.06.02023.097218APARTMENTRENT0000110000000000000010000010110000000100000000001000100000000001000000100100000
1691691100.0103.04.01984.097084APARTMENTRENT0000100000000100000110000010011000000100000000000000000000000010000000000101000
170170715.082.23.01900.097299APARTMENTRENT0010000000100001000110000000001000000000000110000000000000000000100000000100000
171171560.050.02.0NaN97076APARTMENTRENT0000100000000100000010000000001010000100000000000000000000001000000000000000001
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